Combination search strategy-based improved particle swarm optimisation for resource allocation of multiple jammers for jamming netted radar system

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Wei-qi Zou, Chao-yang Niu, Wei Liu, Yan-yun Wang, Jia-qi Zhan
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引用次数: 0

Abstract

This paper presents a new algorithm named combination search strategy (CSS)-based improved particle swarm optimisation (IPSO) to address the resource allocation problem of multiple jammers. First, the authors propose a CSS to effectively broaden the limited search range of the two-step solving framework. This method not only simplifies the solution framework but also considers the combined relationship between beam pointing and transmit power to determine the global solution to the original problem directly. Second, the authors propose the IPSO because the complexity of the decision variables is increased by CSS. This method can change the focus of searching the optimal solution at different stages, correct the direction of particle evolution over time and avoid the interference between the variables. Finally, this study simulates the problem of resource allocation of multiple jammers based on the CSS-IPSO. Based on the simulation results, the combined search strategy can obtain better resource allocation results in a short time, and the IPSO algorithm can further improve the accuracy and stability of the resource allocation results.

Abstract Image

基于组合搜索策略的改进粒子群优化算法用于干扰组网雷达系统的多干扰源资源分配
针对多干扰机的资源分配问题,提出了一种基于组合搜索策略的改进粒子群优化算法。首先,作者提出了一种CSS,以有效地拓宽两步求解框架的有限搜索范围。该方法不仅简化了求解框架,而且考虑了波束指向和发射功率之间的组合关系,直接确定了原始问题的全局解。其次,作者提出了IPSO,因为CSS增加了决策变量的复杂性。该方法可以改变不同阶段搜索最优解的重点,纠正粒子随时间演化的方向,避免变量之间的干扰。最后,本研究模拟了基于CSS-IPSO的多干扰机资源分配问题。基于仿真结果,组合搜索策略可以在短时间内获得更好的资源分配结果,IPSO算法可以进一步提高资源分配结果的准确性和稳定性。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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